Many users may interact with content (e.g., a news article, a social media post, multimedia files, etc.) through computing devices (e.g., smartphones, tablets, augmented reality devices, etc.) on a regular basis. In an example, a user may read an electronic book on a tablet. In another example, the user may search for local restaurants that serve takeout food by reading reviews for the local restaurants. However, users may come across content which they do not understand and/or want to know more about. Thus, users may want to locate additional content related to the content currently being viewed (e.g., an article about a new movie may spark an interest about an actor in the new movie, a local restaurant review for a local deli may make the user want to travel to the local deli, etc.). However, in order to locate additional content, users may be required to exit a first user interface (e.g., a news application) currently displaying the content (e.g., a news article) and perform a search in a second user interface (e.g., a search application) based upon the user's recollection of the content from the first user interface. As a result, users may waste time transitioning back-and-forth between the first user interface and the second user interface. Moreover, users may forget/neglect to search for relevant portions of the content and/or properly define search parameters for the search, which may result in a number of irrelevant search results being presented to users. Unfortunately, many computing devices may lack technology that can efficiently and effectively identify related content, which may result in a decrease in the user's experience with the computing device, an inefficient utilization of computing resources, and/or an inefficient utilization of users time (e.g., users may become frustrated with having to view/sort through a large number of irrelevant search results, etc.).